Spend enough time with sales and marketing leaders, and it’s clear they’re excited about AI. They’ll point to their agents that prep calls in minutes, summarize long-winded meetings into something readable, and produce copy faster than any sales bullpen ever could. And they’ll say their forecasting has never been more “data-driven.”
Keep asking questions, however, and they’ll eventually assert that most of this activity sits on top of the same brittle, fragmented sales and customer data they’ve struggled with for a decade.
Despite this shaky foundation, enthusiasm for AI among sales and marketing leaders is reflected in near-vertical adoption curves. “There isn’t a world now in which AI isn’t an essential part of go-to-market,” says Tim McCormick, a veteran GTM, marketing executive, and CEO in the software industry.
Consider LinkedIn’s 2025 research, which found that more than half of sales professionals now use AI tools daily, and those who do are twice as likely to exceed quota as those who don’t. HubSpot tracked AI adoption among sales reps and found it nearly doubled in a single year, jumping from 24% in 2023 to 43% in 2024. As for marketing, CoSchedule’s survey of over a thousand marketing professionals found that 79% point to efficiency gains as AI’s headline benefit, with content scaling and cost reduction close behind. The tools are fast, the productivity numbers are real, and nobody is going back.
The Big GTM Challenge Is in the Data, Not the AI
Spend any time digging beneath the dashboards and the familiar challenge surfaces: The Interactive Advertising Bureau (IAB) State of Data 2025 report found that nearly two-thirds of marketing and media professionals cite data quality, data protection, and cross-tool fragmentation as their top barriers to operationalizing AI.
Supermetrics’ 2026 Marketing Data Report found the single biggest blocker isn’t talent or budget; it’s fragmented data sitting outside any unified, decision-ready structure. The CRM picture isn’t prettier. Industry research pegs CRM data decay at roughly 30% per year, and surveys consistently find that nearly two-thirds of sales professionals don’t fully trust the data their systems generate.
Gartner finds that only 7% of sales organizations achieve a forecast accuracy of 90% or better. That’s the number hiding behind every confident pipeline review. The AI appears sharp, but the data foundation it runs on in most organizations is not.
Mark Coltharp, GTM expert and general manager at 7AI, recently told DigitalCXO how he uses AI in sales. Coltharp uses agents that pull account research together in minutes, combine intent scores from different providers into a single reliable signal, and auto‑populate fields in their CRM software that sales reps previously ignored. He can lean on call recording systems that track who was in the room, what they focused on, and which competitors were mentioned—without having to rewatch every moment of a sales call. “Forecasts don’t live and die on gut feel; they’re backed by telemetry pulled from calls, emails, and web activity,” says Coltharp.
It sounds like magic until one realizes what goes into making such a workflow possible. To do any of that, the data underneath must at least be coherent. The CRM can’t be a graveyard of half‑filled records and call transcripts, meetings, marketing systems, and product usage analytics must be stitched together with enough consistency that an AI agent can follow a trail.
McCormick, a veteran GTM executive in the software industry, says the old manual work that BDRs did, such as hand‑scoring accounts against an ICP, combing through websites, trying to guess who might care, is being rapidly replaced by systems that can score, segment, and prioritize a universe of accounts much faster than any human team. “That kind of automation only works when the data flowing into it is relatively clean and connected. When it’s not, the AI creates noise faster,” McCormick says.
CXOs Must Build Their AI Readiness Ledger
None of this should surprise any experienced CXO; what’s changed is that go‑to‑market AI has made the quality of commercial data painfully visible. And the risk is that sales and marketing will keep buying tools, wiring them into whatever APIs are easiest to reach, and asking them to “be smart” based on whatever data they can scrape. That’s when the “AI‑ready” rhetoric clashes with reality.
Savvy CXOs will recognize this as one of the clearest openings data and technology leaders have had in years to lead the organization into the future. And to do so with a living, accurate inventory of all the data that the go-to-market teams depend on, and whether that data is fit for purpose.
Call it an AI readiness ledger. At a minimum, such a ledger must answer some simple questions: which primary data‑emitting systems touch customers and prospects—CRM, marketing automation, call recording, product telemetry, support, and third‑party intent? For each, what does the schema look like? How fresh is the data? How bad are the duplicates and the missing fields? How consistently can you follow an account or a buying group from one system to another? Which of these systems are already feeding some AI feature or agent? “It’s easy to get overwhelmed with data,” Coltharp says. “There is a lot of data thrown at you, but if you manage the data right and you use your AI properly, you will be able to pull the key indicators you need to make great decisions,” he says.
They’re the sorts of questions CDOs and CIOs should excel at. But in too many organizations, commercial data is treated as someone else’s mess: a marketing ops curiosity, a RevOps project, or a black box managed entirely by vendors.
The organizations that will get real leverage from AI in go‑to‑market aren’t the ones that buy the most tools. They’re the ones whose data leaders are willing to do the boring, foundational work: mapping the signals, cleaning them up enough that AI can see the patterns, and being honest about where things aren’t quite ready yet. That’s not work sales and marketing can—or should—be left to their own devices.
